Causal Relationships in the Quantitative Social Sciences II

Formalizing the logic of cause and effect

• Potential Outcomes (Donald B. Rubin 1974; Donald B. Rubin 2005)

• Directed Acyclic Graphs (Greenland, Pearl, and Robins 1999; VanderWeele and Robins 2007)

Formal and informal representations of theories of politics

• Connecting research question, data, and theory gives us substance

Important Deadlines

• Deadline to form groups for the final project is Friday, September 30th

  • Groups work on a single paper together–there appears to have been some confusion about this

• Problem Set I is due Friday, September 30th

Correlation: A measure of the extent to which two or more variables tend to occur together.

• If two variables \(\treat{X}\) and \(\resp{Y}\) tend to occur together or increase at the same rate, we would say they are positively correlated

• If the occurrence of \(\treat{X}\) unrelated to \(\resp{Y}\), we would say these two variables are uncorrelated

• If when \(\treat{X}\) occurs we are less likely to observe \(\resp{Y}\), we would say these two variables are negatively correlated

Line of Best Fit: A line that minimizes how far data points are from the line on average, based on some measure of distance from data to the line

Mean \(\mean{\mu}_{\treat{x}}\): \(\frac{\sum_{\obs{i}=\obs{1}}^{\sample{n}} \treat{x}_{\obs{i}}}{\sample{N}}\)

• The average value of a numeric variable

• Deviation from the mean describes the distance between an observation’s value for some variable relative to its mean

Variance \(\vari{\sigma}_{\treat{x}}^{2}\): \(\frac{\sum_{\obs{i}=\obs{1}}^{\sample{n}} (\treat{x}_{\obs{i}} - \mean{\mu}_{\treat{x}})^{2}}{\sample{N}}\)

• A measure of how much variation a variable exhibits. It is the average of the square of the deviations from the mean

Standard Deviation \(\vari{\sigma}_{\treat{x}}\): \(\sqrt{\vari{\sigma}_{\treat{x}}^{2}}\)

• Another measure of the variability in some variable. It is the square root of the variance and has the advantage of being on roughly the same scale as the the variable itself

Covariance \(Cov_{\treat{x}, \resp{y}} = \frac{\sum_{\obs{i}=\obs{1}}^{\sample{n}} (\treat{x}_{\obs{i}} - \mean{\mu}_{\treat{x}})(\resp{y}_{\obs{i}} - \mean{\mu}_{\resp{y}})}{\sample{N}}\)

• A measure of the correlation between two variables. It is calculated as the average of the product of the deviations from the mean

Correlation Coefficient \(\rho_{\treat{x}, \resp{y}} = \frac{Cov_{\treat{x}, \resp{y}}}{\vari{\sigma}_{\treat{x}} \cdot \vari{\sigma}_{\resp{y}}}\)

• Another measure of the (linear) correlation between two variables. It is the obtained by dividing the covariance by the product of the standard deviations

How does \(\treat{X}\) influence \(\resp{Y}\)?

• Good research questions are broad but also precise

• Your research question should clearly state your treatment and outcome of interest

How does the prospect of inclusion for historically dispossessed social groups influence voter turnout?

Do state-level policies restricting abortion care adversely impact access to contraception?

How does the onset of conflict impact a country’s economic growth?

Does American military presence abroad prevent political instability?

Potential Outcomes formally encode counterfactuals

• \(\resp{Y}_{\obs{i}}(\treat{X} = 1)\) is the outcome we would observe if unit \(i\) was treated

• \(\resp{Y}_{\obs{i}}(\treat{X} = 0)\) is the outcome we would observe if unit \(i\) was untreated

Consistency assumption connects observed outcomes to potential outcomes

• \(\resp{Y}_{\obs{i}} = \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}})\) means we observe the potential outcome for the observed treatment

Causal effect for unit \(i\) is \(\resp{Y}_{\obs{i}}(\treat{X} = 1) - \resp{Y}_{\obs{i}}(\treat{X} = 0)\)

• Difference in the observed and potential outcomes

For each individual \(\obs{i}\) we can only observe \(\treat{X}_{\obs{i}} = 1\) or \(\treat{X}_{\obs{i}} = 0\)

It is logically impossible to directly observe \(\resp{Y}_{\obs{i}}(\treat{X} = 1) - \resp{Y}_{\obs{i}}(\treat{X} = 0)\)

We can also generalize this to non-binary treatments (i.e., categorical, continuous, multivariate)

Causal Ordering: \(\treat{X}_{\obs{i}} \longrightarrow \resp{Y}_{\obs{i}}\)

• No reverse causality or simultaneity

Consistency: \(\resp{Y}_{\obs{i}} = \resp{Y}_{\obs{i}}(\treat{x})\) if \(\treat{X}_{\obs{i}} = \treat{x}\)

• There are no hidden versions of the treatment or treatment variance is irrelevant

No Interference between Units: \(\resp{Y}_{\obs{i}}(\treat{X}_{1}, \treat{X}_{2}, \dots, \treat{X}_{\sample{n}}) = \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}})\)

• No causal effect of other units’ treatment on other units’ outcomes

Consistency + No Interference = SUTVA (stable unit-treatment variation assumption)

\(\resp{Y}_{\obs{i}}(\treat{x})\) is the value that \(\resp{Y}\) would take under \(\treat{X}_{\obs{i}}\) set to \(\treat{x}\)

• Common to use uppercase to denote a theoretical quantity and lowercase to denote a realization

In principle, for the causal effect of \(\treat{X}_{\obs{i}}\) to be properly defined, the treatment should be manipulable at least in theory

• There can be no causation without manipulation

This can be complicated for certain immutable characteristics such as race, sex, etc.

• One option is to focus on places where we could, at least in theory, manipulate these characteristics

In a perfect world, we could estimate observation-level causal effects

• \(\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)\)

• In practice this requires strong assumptions and is virtually impossible to identify

Instead, we usually attempt to estimate the average causal effect (ACE) in some subset of the population

Our estimand connects theory to data

Every study must be able to answer the question “What is your estimand?” (Lundberg, Johnson, and Stewart 2021)

Sample Average Treatment Effect (SATE)

\[ \mathrm{SATE} = \frac{1}{\sample{n}}\sum_{\obs{i}=1}^{\sample{n}}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)]\\ = \frac{\sum_{\obs{i}=1}^{\sample{n}}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)]}{\sample{n}} \]

• Average of the outcomes if everyone is treated versus if no one is treated

• Answers the question what “would happen if everyone received the treatment compared to no one receiving it?”

Sample Average Treatment Effect for the Treated (SATT)

\[ \mathrm{SATT} = \frac{1}{\sample{n}_{1}}\sum_{\obs{i}=1}^{\sample{n}}\treat{X}_{\obs{i}}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)]\\ = \frac{1}{\sample{n}_{1}}\sum_{\obs{i}=1}^{\sample{n}}\treat{X}_{\obs{i}}[\resp{Y}_{\obs{i}} - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)]\\ = \frac{\sum_{\obs{i}=1}^{\sample{n}}[\resp{Y}_{\obs{i}} - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)]}{\sample{n}_{1}} \]

Answers the question “What is the average effect of the treatment among those who received treatment?”

SATE and SATT are specific to a given study

• Theoretically, these estimands are well defined without the kind of magical thinking people usually rely on for inference about a population (e.g., repeated sampling)

• They are finite sample or finite population estimands

What if we want to make inferences about a larger population though?

• Assume units are a random sample from a large/infinite population and hedge our bets on the central limit theorem

• In many areas of political science and international relations, this is not a logically defensible assumption

Population Average Treatment Effects (PATE)

\[ \mathrm{PATE} = \mathrm{E}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0)] \]

Population Average Treatment Effect for the Treated (PATT)

\[ \mathrm{PATT} = \mathrm{E}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0) | \treat{X}_{\obs{i}} = 1] \]

In the case of a non-random sample from an apparent population where repeated sampling is logically impossible, \(\mathrm{PATE} = \mathrm{SATE}\) and \(\mathrm{PATT} = \mathrm{SATT}\)

• Arriving at uncertainty estimates is less straightforward since we can’t rely on magical thinking (Berk, Western, and Weiss 1995; Gill and Heuberger 2020; Western and Jackman 1994)

Conditional Average Treatment Effect (CATE)

\[ \mathrm{CATE} = \mathrm{E}[\resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 1) - \resp{Y}_{\obs{i}}(\treat{X}_{\obs{i}} = 0) | \treat{X}_{\obs{i}} = \treat{x}] \]

• We can use this to detect heterogenous effects for theory testing or targeting

We will discuss additional estimands for direct and indirect causal effects later in the course

Causal inference is about counterfactuals

Potential outcomes constitute a way of formally expressing counterfactual relationships

• There are many possible estimands of interest–any possible difference between potential outcomes

Estimands connect research question, theory, and data

• A quantitative study that cannot answer the question “What is your estimand?” is effectively useless

Goal of contemporary political science is to provide vague answers to precise questions

Theoretical relationships as Bayesian networks

• Types of bias

• Difference between direct and indirect effects

Applied example to make this all more concrete

• Replicating Barnes and Holman (2020)

Moving from causal question to causal theory

• If you have not yet at least identified a causal relationship between some \(\treat{X}\) and \(\resp{Y}\) for your paper, you are now behind in the course